AB1848 ARTIFICIAL INTELLIGENCE IN THE CLINICAL SETTING OF THE RHEUMATIC DISEASES; A SYSTEMATIC REVIEW OF THE LITERATURE
Authors: K. Oku, B. Caffo, Y. Arinuma, K. Yamaoka
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Autoimmune Rheumatic Diseases (AIRD) are chronic multifactorial diseases that present with a variety of pathologic findings due to immune abnormalities. The pathogenesis is a complex interplay of genetic and environmental factors, and there are usually large individual differences in phenotype, imaging and blood findings, treatment response, and prognosis. As a result, clear algorithms for diagnosis and treatment are scarce, and clinicians must interpret complex disease images from a variety of data to make analogies between diagnosis, prognosis prediction, and treatment effects. Artificial intelligence, encompasses concepts such as machine learning and deep learning neural networks, is a substitute for these complex tasks, and attempts are gradually beginning to replace it in the clinical practice of AIRD. Accountability is often required for decisions in medicine that directly affect human lives, and the same is true for the black box of models used by AI. Therefore, in recent years, emphasis has been placed on the explainability of AI, and a method called explainable AI (XAI) has emerged to explain decisions made by AI models. Research in this area is still in its infancy, and it would be of significance to present an overall picture. <h3>Objectives</h3> To clarify the overall picture of AI/machine learning in the clinical application or research in AIRD by the systematic review of the literature. <h3>Methods</h3> A systematic literature review was conducted using the Pubmed and Web of Science according to the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses"(PRISMA) guidelines. The search formula used Artificial Intelligence, Machine learning, Deep learning, Neural Network, AIRD and connective tissue diseases as main keywords, and selected references went through a primary/secondary screening. After screening, the literature was reviewed to validate the diseases studied, published years, the object of the study, type of training data, the algorithms, and, for XAI, the methods for algorithms explanation. <h3>Results</h3> The literature search yielded 1483 articles, that were published before June, 2022 and resulted in 164 articles after primary and secondary screenings. Among them, 153/163(93.9%) were published in or after 2017. The main target diseases of the articles were rheumatoid arthritis(RA) 72, systemic lupus erythematosus(SLE) 42, spondyloarthritis 11, inflammatory myositis 7, systemic vasculitis syndrome 5, systemic sclerosis 5 and others 21. The main objects of the studies were diagnosis or classification of the disease in 98, followed by the prognosis prediction in 49 and analysis of the therapeutic reagent in 19. The most common types of data used as training were demographic data in 114 articles followed by physical data 101, clinical test data 92, genomics data 40, image data 30 and pathological data 28. The most commonly used algorithms were tree-based algorithm(random forest etc.) followed by support vector machine and neural network. XAI were performed in 8 studies (3 studies with RA, 3 with SLE one with SLE/RA and 1 with AIRD in general) in which Shapley Additive exPlanations (SHAP) was the most frequently used tool (5/8; 62.5%). <h3>Conclusion</h3> The clinical application of AI in AIRD is mainly used as a tool for diagnosis or the prognostic prediction in RA and SLE. On the other hand, only a very small number of the study have examined the explainability of AI. The review indicates that research on the clinical implementation of AI applications is still at an early stage despite the great potential. <h3>REFERENCES:</h3> NIL. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.